Serial Exponential Sea Horse Optimization Algorithm for Container Scheduling in Cloud Architecture

The primary objective of this work is to present a novel hybrid optimization approach for container scheduling in cloud environments. Cloud architecture is based on a virtualized layer above Physical Machines (PMs) using Virtual Machines (VMs) and containers to promote scalability and flexibility. Containers, due to their lightweight nature relative to VMs, share the same host OS kernel, lowering overhead and speeding up startup time. With all dependencies accounted for, containers make deployment across environments easier, improve resource usage, and secure isolation applications. These characteristics make containers ideal for cloud systems, where quick deployment, resource efficiency, and scalability are critical. Here, a new efficient hybrid optimization algorithm, called Serial Exponential Sea-horse Optimizer (SE-SHO) is proposed for optimized container scheduling in cloud environment. The proposed SE-SHO is created by combining Exponential Weighted Moving Average (EWMA) and Sea-horse Optimizer (SHO) to make the convergence faster as well as to increase accuracy and robustness during complex scheduling tasks. Here, the objective function is generated on the basis of the load, resource utilization, energy consumption, and transmission cost. The experimental analysis stated that the proposed approach reached a migration cost of 0.183, a makespan of 0.423 and resource utilization of 0.482.